class_docstr: Conv3dResNet module
class_docstr_tok: ["Conv3dResNet","module"]
docstr: forward.
  :param xs_pad: torch.Tensor, batch of padded input sequences.
docstr_tok: ["forward",".",":","param","xs_pad",":","torch.Tensor",",","batch","of","padded","input","sequences","."]
code: def forward(self, xs_pad):
      """forward.
      :param xs_pad: torch.Tensor, batch of padded input sequences.
      """
      # -- include Channel dimension
      xs_pad = xs_pad.transpose(2, 1)
      B, C, T, H, W = xs_pad.size()
      xs_pad = self.frontend3D(xs_pad)
      Tnew = xs_pad.shape[2]  # outpu should be B x C2 x Tnew x H x W
      xs_pad = threeD_to_2D_tensor(xs_pad)
      xs_pad = self.trunk(xs_pad)
      xs_pad = xs_pad.view(B, Tnew, xs_pad.size(1))
      return xs_pad
  
file_path: audio/examples/avsr/models/resnet.py
imports: import torch.nn
label_desc: Use image node_id 2 for calling the Conv3dResNet obj's underlying member method code with example usage: obj.forward(xs_pad) and returns: xs_pad